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Related Experiment Video

Updated: Jul 4, 2026

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

Integrated multicenter deep learning system for prognostic prediction in bladder cancer.

Quanhao He¹, Bangxin Xiao¹, Yiwen Tan²

¹Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, P. R. China.

NPJ Precision Oncology

|October 16, 2024

Summary

This study introduces a deep learning system for bladder cancer (BCa) survival risk prediction using histological slides. The AI model accurately stratifies patients, improving personalized therapy and management for better outcomes.

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Last Updated: Jul 4, 2026

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

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Published on: January 12, 2017

3-D Cell Culture System for Studying Invasion and Evaluating Therapeutics in Bladder Cancer

3-D Cell Culture System for Studying Invasion and Evaluating Therapeutics in Bladder Cancer

Published on: September 13, 2018

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Accurate survival risk stratification is essential for personalized bladder cancer (BCa) therapy.
Current methods may lack the precision needed for optimal patient management.

Purpose of the Study:

To develop and validate an end-to-end deep learning system for predicting overall survival (OS) risk in BCa patients using histological slides.
To assess the system's performance and its potential to improve patient stratification.

Main Methods:

Utilized a BlaPaSeg tile classifier to generate tissue probability heatmaps and segmentation maps.
Trained two prognostic deep learning networks, MacroVisionNet and UniVisionNet.
Explored six potential BCa prognostic biomarkers, including Tumor Co-localization score (Coloc) and Integrated Muscle Tumor Score (IMTS).

Main Results:

The BlaPaSeg classifier demonstrated high accuracy (AUC 0.9906–0.9945).
MacroVisionNet and UniVisionNet achieved significant C-indices (0.655–0.853) and predicted higher mortality risk in high-risk groups (HR 1.97–5.06 and 2.13–4.01, respectively).
High-risk Coloc and IMTS groups showed significantly increased death risk (HR 1.41–10.16).

Conclusions:

The developed deep learning system effectively predicts overall survival risk in bladder cancer patients.
This AI-driven approach enhances survival prediction accuracy, supporting refined patient management and personalized treatment strategies.